AI-Powered DevOps with LLMs : Applying Large Language Models to Software Delivery and SRE

Huangliang, Gu/ Qingzheng, Zheng/ Xiaoling, Niu

Packt Publishing Limited（2026/05発売）

• 製本 Paperback:紙装版/ペーパーバック版
• 言語 ENG
• 商品コード 9781807609191

Full Description

A practical guide to applying large language models across the DevOps lifecycle to improve automation, efficiency, and reliability

Key Features

Use large language models to enhance DevOps workflows across development, testing, and operations
Implement GPT, fine-tuning, RAG, and agent-based systems with practical enterprise examples
Boost R&D efficiency, automation, and reliability in modern software delivery pipelines

Book DescriptionLarge language models (LLMs) are rapidly transforming how software is built, delivered, and operated. Practice of Research and Development Efficiency Driven by Large Models provides a comprehensive and practice-oriented guide to applying LLMs across the full DevOps lifecycle from development and testing to operations, security, and project management.
Starting with the foundations of large language models, Transformers, and GPT architectures, you will progresses to advanced topics such as fine-tuning techniques (LoRA, QLoRA, PEFT), retrieval-augmented generation (RAG), and agent-based systems. You will then see how these technologies can be applied in real-world DevOps scenarios, including intelligent operations, automated testing, code generation, incident analysis, and project delivery optimization.
With extensive case studies drawn from enterprise environments, this book bridges theory and practice, helping you improve R&D efficiency, automation, reliability, and decision-making using large language models.What you will learn

Understand the evolution of large language models and Transformer-based architectures
Build and optimize GPT-style models, including fine-tuning and reinforcement learning techniques
Apply RAG and agent architectures to enterprise DevOps and platform engineering scenarios
Use LLMs to automate operations tasks such as log analysis, ticket handling, and root cause analysis
Enhance testing, programming, and CI/CD workflows with large language models
Apply LLMs to project management, risk analysis, and security use cases in DevOps environments

Who this book is forThis book is aimed at DevOps practitioners, including AI researchers, developers, project managers, and operations engineers. It explores how large language models enhance automation, CI/CD pipelines, software delivery, and operational reliability across modern DevOps environments.

Contents

Table of Contents

Introduction to Large Language Models
The Cornerstone of Large Language Models—Transformer
From Transformer to ChatGPT33
Fine-Tuning Techniques for Large Language Models
Enterprise AI Application Technology— RAG
Three Foundational Pillars of Software Delivery
Practical Applications of Large Language Models in Operations Scenarios
Practical Applications of Large Language Models in Testing Scenarios
Practical Applications of Large Language Models in Programming Scenarios
Practical Applications of Large Language Models in Project Management Scenarios
Practical Applications of Large Language Models in Security Scenarios